Open Journal of Forestry, 2020, 10, 66-80 https://www.scirp.org/journal/ojf

ISSN Online: 2163-0437 ISSN Print: 2163-0429

DOI: 10.4236/ojf.2020.101006 Dec. 25, 2019 66 Open Journal of Forestry

LiDAR Helps Differentiate Stand Health and Productivity Levels within a Northern Hardwood Forest

Christopher F. Hansen1, Paul G. Schaberg2*, Allan M. Strong1, Shelly A. Rayback3, Gary J. Hawley1

1Rubenstein School of Environment and Natural Resources, The University of Vermont, Burlington, VT, USA 2USDA Forest Service, Northern Research Station, Burlington, VT, USA 3Geography Department, The University of Vermont, Burlington, VT, USA

Abstract

Light detection and ranging (LiDAR) data can provide detailed information about three-dimensional forest structure. However, links between forest structure and tree function have not been fully evaluated using LiDAR. We assessed the relationship of LiDAR-derived structural categories to tree health and productivity on 36 hardwood plots at the Hubbard Brook Experimental Forest, New Hampshire, USA. We established nine plot replicates for each of four LiDAR-based vegetation categories: 1) high crown and high understory closure; 2) high crown and low understory closure; 3) low crown and high understory closure; and 4) low crown and low understory closure. Ground-based measures of canopy structure, site, stand and individual tree measures were collected on plots during summer 2012. Significant differences among LiDAR categories were found for several response variables. Lower basal area incre-ment for sugar maple (Acer saccharum), decreased foliar nutrition for yellow birch (Betula alleghaniensis), and lower overall crown health were all asso-ciated with high understory closure provided that overstory closure was also high. These results suggest that LiDAR measures can be used to assess com-petitive interactions between overstory and understory vegetation, and that LiDAR shows promise for identifying stands with reduced health and prod-uctivity due to factors such as competition or overstocking.

Keywords

Basal Area Increment, Crown Health, Foliar Nutrition, Tree Function, Tree Mortality

How to cite this paper: Hansen, C. F., Schaberg, P. G., Strong, A. M., Rayback, S. A., & Hawley, G. J. (2020). LiDAR Helps Differentiate Stand Health and Productivity Levels within a Northern Hardwood Forest. Open Journal of Forestry, 10, 66-80. https://doi.org/10.4236/ojf.2020.101006 Received: November 16, 2019 Accepted: December 22, 2019 Published: December 25, 2019 Copyright © 2020 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/

Open Access

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1. Introduction

Managers and ecologists must develop cost-effective techniques to evaluate fun-damental forest attributes that influence forest health and productivity, such as stand structure and complexity, and canopy closure. This is especially critical in the face of climate, environmental, and land-use changes that may alter the structure and function of forest systems. Light detection and ranging (LiDAR) remote sensing is a technique that may be useful for evaluating such attributes. LiDAR technology can be used to map vegetation structure in three dimensions, as well as record spectral intensity at landscape scales at relatively low costs compared to field-based measures (Lefsky et al., 2002; Sherrill et al., 2008; Luo et al., 2017).

The relationship of LiDAR to forest structure and function has only recently been assessed, with relatively few studies evaluating the relationship between LiDAR and specific measures of stand- and tree-based health and productivity. For example, Vitousek et al. (2009) evaluated the relationship of LiDAR-derived canopy vertical structure to remotely sensed measures of foliar chemistry and found clear relationships between canopy height and nitrogen (N) concentra-tions. Other studies have utilized LiDAR to quantify forest structure and canopy functional traits (i.e., normalized difference vegetation index—NDVI) across elevation gradients (Asner et al., 2014), as well as relating LiDAR-derived gap frequency and forest structure to edaphic and geological patterns (Higgins et al., 2015). These studies evaluated LiDAR at broad landscape-scales, with no ground-based measures for direct comparisons. By contrast, Gökkaya et al. (2015) evaluated the utility of LiDAR in conjunction with spaceborne imaging spectroscopy in predicting ground-based measures of foliar macronutrients at the canopy level in mixed wood boreal forests. Their analysis found that the in-clusion of LiDAR data with imaging spectroscopy significantly improved predic-tion accuracy of canopy macronutrients. They identified crown closure and ca-nopy height as being the structural metrics most important in predicting canopy macronutrient concentrations. Similarly, in forests characterized by high struc-tural heterogeneity, Ewald et al. (2018) also compared ground-based measures of N and phosphorus to remotely-sensed data, finding that LiDAR-derived va-riables improved model predictions from imaging spectroscopy alone.

Our research was (originally) designed (and scaled) to determine the rela-tionship between high-resolution LiDAR and various ecosystem processes, in-cluding the abundance and diversity of arthropod and avian communities (Hansen, 2015). However, we also added plot-based measures to assess the rela-tionship of LiDAR to tree health and productivity, with a particular emphasis on measures of tree function and health. We hypothesized that LiDAR can be used to quantify structural characteristics that are either the cause (e.g., overcrowd-ing) or the consequence (e.g., greater crown thinning or tree mortality) of tree health and productivity issues. Therefore, LiDAR data should reflect compo-nents of tree-based measures that are influenced by forest structure and vegeta-

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tion density (e.g., competitive interactions among canopy strata for limited re-sources), which if verified, would support the use of LiDAR to generate proxy measures of forest health and productivity. The resulting analysis provides the first evaluation of the relationship between forest structure derived from LiDAR and the health and productivity of northern hardwood trees.

2. Methods 2.1. Study Site, LiDAR Processing and Field Plot Selection

This research was conducted at the Hubbard Brook Experimental Forest (HBEF) in Thornton, New Hampshire, USA (43˚56'N, 71˚45'W; Figure 1). Elevations at HBEF range from 222 to 1015 m, and forests below 750 m are dominated by northern hardwood species—primarily sugar maple (Acer saccharum Marsh.), American beech (Fagus grandifolia Ehrh.), and yellow birch (Betula alleghanien-sis Britton).

High-resolution discrete return LiDAR data (flown leaf-on, September 1, 2009) were acquired for HBEF. These data were originally collected by the Ca-naan Valley Institute, West Virginia, for the University of Maryland, Depart-ment of Geography using an Optech ALTM 3100 flown at an altitude of ap-proximately 1065 m above ground level (AGL). These data were received in raw (LAS 1.0) point cloud format with no associated surface models. Using Quick

Figure 1. Study site and plot selection: (a) LiDAR crown and understory closure catego-ries, (b) canopy height (nDSM) and 0.5 - 10 m AGL surface models, and (c) Hubbard Brook Experimental Forest, NH, USA—showing the 36, 200 m × 200 m blocks. LiDAR surface models were derived from LiDAR point cloud data acquired for the Hubbard Brook Experimental Forest and originally collected by the Canaan Valley Institute, West Virginia, for the University of Maryland, Department of Geography.

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Terrain Modeler (Applied Imagery, Inc., Silver Spring, MD) and ERDAS Im-agine (Intergraph Corporation, Inc., Madison, AL), we transformed LiDAR point cloud data into a normalized digital surface model (nDSM) to represent canopy height, a digital elevation model (DEM) to represent ground elevation, and a 0.5 - 10 m AGL surface model to represent the presence or absence of ve-getation within the canopy’s understory vertical plane.

Un-adjustable variations in LiDAR data involving the overlap of adjacent flight lines necessitated the conversion of raw LiDAR point clouds into surface models from which canopy and understory closure categories were created. We used eCognition, an object-oriented segmentation software (Trimble Geospatial, Inc., Westminster, CO), to identify two understory closure categories (high un-derstory [55% - 78.15%] and low understory [43% - 54.99%] vegetation closure in the 0.5 - 10 m AGL class) and two crown closure categories (high crown clo-sure [94% - 98.09%] and low crown closure [89.89% - 93.99%]). Canopy and understory categories were operationally defined to create breakpoints that di-vided the forest area assessed into two equal-sized groups per canopy strata. This resulted in four combinations of understory and crown closure categories: 1) high crown closure and high understory closure (hereafter referred to as HH), 2) high crown closure and low understory closure (HL), 3) low crown closure and high understory closure (LH), and 4) low crown closure and low understory closure (LL) (Figure 1).

LiDAR-derived surface models were used in conjunction with pre-existing geospatial vector data (HBEF data archives—http://data.hubbardbrook.org/gis/) to identify areas of deciduous and mixed deciduous forest types within an eleva-tion threshold of roughly 400 - 800 m. A 200 m × 200 m grid was placed over the resultant area to represent 4 ha sampling blocks for potential use. This block size was chosen because it approximated the territory size of black-throated blue warblers (Setophaga caerulescens) (Holmes et al., 2005), as one component of the broader study was to relate LiDAR data to avian habitat. Sampling blocks were then randomly selected based on the four LiDAR-derived canopy structure classifications, with nine replicates containing each of the four categories located in close proximity to account for any spatial clustering that might affect response variables—resulting in a total of 36, 200 m × 200 m blocks (Figure 1).

2.2. Crown Health and Basal Area

Within each of these blocks, we established a 50 m fixed radius plot that fol-lowed the standard design and protocols of the Forest Inventory and Analysis (FIA) program, including vegetation inventories on plots, subplots and mi-cro-plots (Bechtold & Scott, 2005). For all inventories, canopy health was as-sessed via crown vigor index, and percent branch dieback measures were esti-mated according to the methods of the North America Maple Project (Cooke et al., 1996). Crown vigor index employs a 1 - 5 scale, where ratings range from vi-gorous crowns (1) to dead crowns (5). Percent branch dieback was estimated using a 12-class system. Basal area (BA) per hectare was calculated for both live

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(2.5 - 12.5 cm diameter breast height (DBH) on micro-plots) and dead trees (>12.5 cm DBH on subplots) according to FIA protocol. All field-based data were collected during summer and fall 2012.

2.3. Basal Area Increment

At the plot level, five dominant or co-dominant sugar maple and five dominant or co-dominant yellow birch trees were selected for intensive tree-based meas-ures. American beech was not selected due to the confounding influence that beech bark disease has had on the health and productivity of this species (Hal-man et al., 2014). Diameter at breast height (DBH) and visual assessments of canopy health were quantified, and xylem increment cores were obtained for each tree. Two increment cores were collected at DBH for each tree using a 5 mm increment borer to estimated tree age and assess annual growth. Cores were prepared using standard dendrochronological techniques (Stokes & Smiley, 1968) and crossdated visually using the methods of Yamaguchi (1991). Tree rings were microscopically measured (0.001 mm resolution using a Velmex sliding stage unit [Velmex Inc., Bloomfield, NY] with Measure J2X software [VoorTech Consulting, Holderness, NH]) and statistically crossdated using the program COFECHA (Holmes, 1983). Chronology statistics (i.e., series intercor-relation, autocorrelation, and average mean sensitivity) were obtained from COFECHA by plot and species and used to calculate the expressed population signal (EPS; Wigley et al., 1984). To maximize the length of our dataset chro-nologies, we used an EPS value of 0.80 to achieve a uniform sample chronology from 1950-2012 for both species and all plots (Hansen, 2015). Tree age at breast height was calculated using the maximum number of rings per tree if pith was evident or estimated per core using a pith indicator (Speer, 2010). For all trees, we were able to estimate age from at least one core.

Individual tree-ring width chronologies were converted to basal area incre-ment (BAI) according to the methods of Speer (2010). Individual BAI chronolo-gies were then averaged per plot by species to create plot means that could be related to other plot-based data. Sugar maple and yellow birch BAI measure-ments were evaluated in relation to LiDAR categories for multiple time periods: mean BAI for 2009 (the year of LiDAR collection), mean BAI for the 1970s (i.e., 1970-1979) and 2000-2012. The 1970s was the period of peak inputs of acidic deposition in the northeastern US prior to amendments to the US Clean Air Act (Driscoll et al., 2001) and have been identified as an important turning point in the trajectory of sugar maple BAI growth (Drohan et al., 2002). The time period 2000-2012 was assessed to evaluate LiDAR’s relationship to current BAI growth as inputs of acid deposition have decreased.

2.4. Foliar Nutrition

Foliar cations were assessed on sunlit/upper canopy foliage collected using shotguns in early August 2012 (Huggett et al., 2007). Samples were collected from the five sugar maple and five yellow birch per plot sampled for other

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tree-based measures. Foliage was oven-dried at 55˚C for two weeks and ground using a Wiley Mill with a 2 mm mesh. Ground foliage was then run through a series of nitric acid and hydrogen peroxide digestions (Huggett et al., 2007). Ca-tion concentrations (calcium (Ca), aluminum (Al), potassium (K), P, manganese (Mn), and magnesium (Mg)) were measured from the digested foliage using in-ductively coupled plasma atomic emission spectrometry (Perkin-Elmer Optima DV 3000; Perkin-Elmer, Norwalk, CT) and expressed as mg/kg. Percent foliar carbon (C) and N were also assessed for two randomly selected sugar maple and two yellow birch trees per plot using a CHN-CE440 element analyzer (Exeter Analytical, Inc. North Chelmsford, MA). Peach leaf standards (National Insti-tute of Standards and Technology SRM 15547) and blanks were processed for analytical comparisons.

2.5. Statistical Analyses

Analysis of variance (ANOVA) was used to test for differences among LiDAR categories. When significant differences among LiDAR categories existed for response variable means, specific differences among means were assessed using Tukey HSD tests. Regression analyses were used to assess trajectories in growth through 1979 and after 1980—the approximate start of sugar maple decline at HBEF (Fahey et al., 2005; Juice et al., 2006). Analyses were performed using the statistical package JMP (SAS Institute, Inc., Cary, NC), with results being consi-dered significantly different if P ≤ 0.05, unless otherwise noted.

3. Results and Discussion 3.1. LiDAR Assessments of Crown Condition, Tree Mortality and

BAI Growth

Percent branch dieback was significantly different among LiDAR categories, with HH and LL categories exhibiting greater dieback than the HL category (Table 1). Crown vigor index was only marginally different among LiDAR cat-egories (P ≤ 0.10), with a trend for LiDAR categories with high understory clo-sure having a greater crown vigor index (poorer condition) regardless of crown closure (Table 1). Standing-dead basal area was also marginally different (P ≤ 0.10) among LiDAR categories, with a tendency for plots with HL closure to ex-hibit the least standing-dead basal area (Table 1). In comparison, basal area of live understory and intermediate trees (DBH 2.5 - 12.5 cm) was significantly greater (P ≤ 0.05) on HH plots compared to all other LiDAR categories, with LH plots also exhibiting greater live basal area despite showing no statistical differ-ence from HL and LL plots (Table 1).

Measures of BAI were significantly different among LiDAR categories for sugar maple but not yellow birch (Table 2). For 2009 (year of LiDAR), BAI growth for sugar maple in HH plots was significantly lower than maples in LL plots, with growth in HL and LH being intermediate. For the recent period (2000-2012), maple growth in HH plots was lower than growth on HL plots,

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Table 1. Mean (±SE) plot level percent branch dieback and crown vigor index, micro-plot live basal area, and sub-plot standing dead basal area by LiDAR category, collected during 2012 at the Hubbard Brook Experimental Forest, NH, USA.

LiDAR crown & understory closure category

Response variable Significance

High crown High crown Low crown Low crown

High understory Low understory High understory Low understory

(HH) (HL) (LH) (LL)

Decline

% branch dieback ** 31.60 ± 2.27a 23.86 ± 1.48b 30.52 ± 3.03ab 31.17 ± 2.12a

Crown vigor index * 2.34 ± 0.09 1.97 ± 0.07 2.27 ± 0.16 2.22 ± 0.10

Basal area (m2/ha)

Live (2.5 - 12.5 cm DBH) ** 4.33 ± 0.64a 2.27 ± 0.56b 2.48 ± 0.50b 2.33 ± 0.64b

Standing dead (>12.5 cm DBH) * 6.26 ± 1.30 2.46 ± 0.50 5.13 ± 1.03 4.86 ± 1.15

**and in bold P ≤ 0.05, *P ≤ 0.10. Means (±SE) with differing letters are statistically significantly different based on Tukey HSD tests.

Table 2. Mean (±SE) basal area increment (cm2) for sugar maple and yellow birch trees by LiDAR category collected at the Hub-bard Brook Experimental Forest, NH, USA.

LiDAR crown & understory closure category

Response variable Significance

High crown High crown Low crown Low crown

High understory Low understory High understory Low understory

(HH) (HL) (LH) (LL)

Basal area increment (cm2)

Sugar Maple:

2000-2012 ** 7.05 ± 1.58b 11.09 ± 0.96a 10.92 ± 1.58ab 10.33 ± 1.99ab

2009 (year of LiDAR acquisition) ** 7.73 ± 1.74b 11.69 ± 0.98ab 11.41 ± 1.42ab 12.44 ± 2.66a

1970’s (1970-1979) ** 11.05 ± 1.44b 18.50 ± 2.66a 18.44 ± 0.97a 15.45 ± 1.72ab

Yellow Birch:

2000-2012 NS 10.93 ± 1.44 10.99 ± 1.42 12.01 ± 1.64 11.37 ± 0.59

2009 (year of LiDAR acquisition) NS 15.13 ± 2.20 14.36 ± 1.38 15.56 ± 2.36 14.86 ± 0.93

1970’s (1970-1979) NS 12.24 ± 1.24 15.02 ± 1.94 14.21 ± 2.28 13.50 ± 1.54

**and in bold P ≤ 0.05, “NS” denotes not significant. Means (±SE) with differing letters are statistically significantly different based on Tukey HSD tests.

with growth on LH and LL plots being intermediate (Table 2). In addition, sugar maple BAI growth for the 1970s was significantly lower on HH plots compared to HL and LH plots, with LL plots being intermediate. Growth trajectories over time also diverged among maples on HH versus HL plots. Maples on HH plots showed lower and stable growth before 1980 whereas maples on HL plots had greater and increasing growth during this period (Figure 2(a)). After 1980, the growth of maples on both HH and HL plots declined, though this reduction was more dramatic on HL plots (Figure 2(a)). There was a tendency for BAI to be lower for yellow birch on HH compared to HL plots (Figure 2(b)), but differ-ences among means were not significant (P = 0.4687). Although maple BAI

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(a)

(b)

Figure 2. Mean basal area increment (BAI; ± SE) for (a) sugar maple and (b) yellow birch trees on HH and HL LiDAR plots at the Hubbard Brook Experimental Forest, NH, USA. BAI was significantly different between HH and HL plots over the length of the chronol-ogy for sugar maple (P = 0.0064) but not for yellow birch trees (P = 0.4687). Regression equations are listed for sugar maple growth trajectories before and after 1980, approx-imately when decline began (Fahey et al., 2005; Juice et al., 2006). P-values indicate whether regressions slopes are significantly different from zero. differed among LiDAR categories, estimated tree age did not differ among Li-DAR groups for either sugar maple (P > 0.10) or yellow birch (P > 0.22), and tree ages for the HH and HL categories were nearly identical (Hansen, 2015).

Overall, HH plots exhibited significantly greater percent dieback and tended to have poor crown vigor and greater standing dead basal area, as well as greater live basal area for trees 2.5 - 12.5 cm DBH, than HL plots (Table 1). LiDAR cat-

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egories also showed utility in differentiating annual BAI growth for sugar maple. In two of the three time periods assessed, differences in sugar maple growth mirrored general trends in crown condition and standing dead basal area; growth was significantly lower in HH plots compared to HL plots, with LH and LL plots generally being intermediate (Table 2).

Because HH plots had the greatest crown closure and understory density, re-duced tree performance in these plots likely reflects the consequences of compe-tition for some limiting resource(s). In particular, the lower health (Table 1) and growth (Table 2) of sugar maple in HH compared to HL plots may indicate that, at least when canopy closure is high, added competition from understory vegeta-tion may reduce sugar maple vigor and productivity. On our plots, four woody species dominated the understory: Fagus grandifolia, Viburnum alnifolium, Acer pensylvanicum and Acer saccharum. All had average densities over 10,000 stems/ha (Hansen, 2015). Competition between canopy and understory vegeta-tion can have a meaningful influence on plant systems (Woods, 1984), including the reduced growth of overstory trees (Busse et al., 1996). Indeed, this concept of competitive interactions between overstory and understory trees and shrubs is one reason why silvicultural practices such as thinning-from-below (Barnes et al., 1997) and prescribed burning (Elliott et al., 1999) are used to control woody understory vegetation.

Competition for light is unlikely affecting tree growth because greater un-derstory plant densities would not increase competition for light with overtop-ping canopy trees. Furthermore, even though sugar maples are known to be drought sensitive (Horsley et al., 2002), it seems unlikely that competition for water would be a dominant factor driving competitive interactions here because measured soil moisture levels did not differ among the LiDAR categories (Han-sen, 2015) and because the northeastern US has been in a pluvial period since the end of a regional drought in the 1960s (Pederson et al., 2013). Indeed, dif-ferences in sugar maple BAI between HH and HL categories were comparatively low during the 1960s, but increased thereafter (Figure 2(a)). Thus, several lines of evidence suggest that water should be generally abundant and not a factor li-miting tree growth. By contrast, considering the well-established nutritional li-mitations of the northern hardwood forests of HBEF (Likens et al., 1998), we propose that competition for nutrients between understory and overstory plants may be responsible for the differences in health and productivity we detected among LiDAR plots. Belowground competition from shrub roots may be most important in nutrient-poor sites (Montgomery et al., 2010), and Rolo et al. (2012) found that overstory trees growing with a shallow rooted shrub could also experience Ca limitations – a potential response with particular relevance for the Ca-limited forests of the HBEF (Likens et al., 1998).

3.2. Foliar Nutrition

No differences in foliar cation concentrations or percent N and C were found for sugar maple among LiDAR categories (Table 3). Nonetheless, foliar cation

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Table 3. Mean (±SE) foliar Ca, Mg, Mn, Al, and Ca:Al molar ratio (mg∙kg−1) and percent C and N for sugar maple and yellow birch trees by LiDAR category collected at Hubbard Brook Experimental Forest, NH, USA.

LiDAR crown & understory closure category

Foliar nutrition (mg∙kg−1)

Significance

High crown High crown Low crown Low crown

High understory Low understory High understory Low understory

(HH) (HL) (LH) (LL)

Sugar maple:

Ca NS 4965.89 ± 565.79 5534.03 ± 192.17 5650.07 ± 626.43 5214.28 ± 429.92

Al NS 69.85 ± 10.95 63.48 ± 9.13 70.85 ± 11.66 72.39 ± 10.33

K NS 7470.56 ± 366.76 8152.25 ± 481.53 8311.45 ± 452.42 7911.42 ± 454.05

P NS 1455.37 ± 175.26 1546.09 ± 222.51 1347.03 ± 136.33 1598.29 ± 215.66

Mg NS 976.82 ± 107.59 1045.7 ± 50.46 1035.3 ± 107.52 972.79 ± 89.55

Mn NS 1307.39 ± 192.33 1020.60 ± 104.65 1324.35 ± 169.05 989.51 ± 108.72

Ca:Al Molar Ratio NS 55.71 ± 8.91 70.41 ± 11.09 70.14 ± 18.9 55.91 ± 8.49

C (%) NS 48.82 ± 0.72 48.32 ± 0.50 47.61 ± 0.41 47.59 ± 0.45

N (%) NS 2.45 ± 0.08 2.49 ± 0.05 2.50 ± 0.08 2.25 ± 0.07

Yellow birch:

Ca ** 7966.97 ± 489.31b 10122.90 ± 667.88a 7969.18 ± 384.25b 8757.82 ± 472.02ab

Al NS 68.13 ± 8.32 59.01 ± 9.34 61.31 ± 9.24 51.73 ± 6.93

K NS 9653.69 ± 748.75 11814.4 ± 972.45 11776.79 ± 714.89 11289.71 ± 974.94

P NS 1425.87 ± 108.95 1479.61 ± 79.13 1291.43 ± 80.48 1729.22 ± 226.74

Mg NS 2347.36 ± 140.4 2479.96 ± 132.97 2197.16 ± 88.13 2323.18 ± 125.26

Mn NS 1794.31 ± 259.1 1727.24 ± 200.49 1522.94 ± 251.59 1322.55 ± 126.98

Ca:Al Molar Ratio * 85.06 ± 8.35b 132.98 ± 16.67a 101.31 ± 13.83ab 130.84 ± 18.4ab

C (%) NS 48.19 ± 0.63 47.67 ± 0.49 47.13 ± 0.4 47.61 ± 0.67

N (%) NS 2.86 ± 0.06 2.84 ± 0.11 2.91 ± 0.09 2.79 ± 0.09

**and in bold P ≤ 0.05, *P ≤ 0.10, “NS” denotes not significant; Means (±SE) with differing letters are statistically significantly different based on a Tukey HSD test.

concentrations across LiDAR groups often deviated from levels considered healthy for sugar maple trees (Kolb & McCormick, 1993). For example, Mg was lower and Al was greater than typical levels, whereas foliar Ca concentrations were below healthy levels in HH plots and slightly above these levels in other LiDAR categories. Furthermore, the molar ratios of Ca:Al were well below those reported for vigorous sugar maple trees. Long et al. (1997) suggested that any-thing less than a Ca:Al molar ratio of 110 was associated with sugar maple de-cline, whereas Ca:Al levels were well below 75 for the sugar maples that we as-sessed (Table 3). Data from ambient and cation-treated sites have established that these changes in soil cation availability have altered tree Ca and Al nutri-tion, and impaired the physiology and growth of sugar maple trees (e.g., Horsley et al., 2002; Huggett et al., 2007). We propose that the dominance of ac-

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id-induced leaching resulted in fairly uniform and marginally sufficient Ca and elevated Al foliar concentrations that overwhelmed variation among our LiDAR categories, but which predisposed maples to reduced growth after 1980 (Figure 2(a)).

For yellow birch only, foliar Ca concentrations and Ca:Al molar ratio differed significantly; HL plots exhibited significantly greater foliar Ca than HH and LH plots, with LL plots being intermediate, and Ca:Al molar ratios were significantly greater on HL plots compared to HH plots, with LH and LL being intermediate. To our knowledge, there are no published ranges for healthy foliar cation con-centrations for yellow birch. However, comparison of our data (Table 3) to the means reported in the Northeastern Ecosystem Research Cooperative foliar chemistry database (NERC, 2016) indicated that the concentrations that we quantified were close to (<1 SD from) regional means reported for all cations and C, with the exception of K, Al, and percent N (which were slightly > 1 SD above regional means). Furthermore, yellow birch foliage generally accumulated greater concentrations of all cations other than P and Al than did sugar maple trees (Table 3). Similar to health and productivity measures, foliar Ca concen-trations in yellow birch were significantly lower in HH plots than in HL plots, with concentrations in LH and LL plots tending to be intermediate (Table 3). This could indicate that in areas with high crown competition, yellow birch with less understory competition have greater access to Ca.

4. Conclusion

LiDAR has been shown to be a useful tool in assessing basic measures of forest structure, such as canopy height, basal area, and leaf area index (Næsset, 2007; Jensen et al., 2008; Pearse et al., 2017; Luo et al., 2017). However, the ability of LiDAR to bridge the gaps between forest canopy structure and tree health and productivity measures has only recently been evaluated, and principally in rela-tion to canopy chemistry (e.g., Vitousek et al., 2009; Gökkaya et al., 2015; Ewald et al., 2018). Our results show the novel ability of LiDAR to assess stand condi-tion and function via competition between overstory and understory vegetation. In our study, these differences were primarily detected for plots with closed ca-nopies that varied in understory closure, suggesting that increased competition from understory plants reduced overstory vigor, sugar maple BAI growth, and yellow birch foliar Ca concentrations. However, at locations where other site and competitive factors influence stand structure and function, we expect that Li-DAR estimates could be associated with an even broader range of stand- and tree-based measures of health and productivity. In addition, LiDAR continuous point cloud data had to be converted to categorical LiDAR classes, which un-doubtedly simplified and minimized the informational content of estimated for-est structure. Despite these limitations, LiDAR estimates were associated with various measures of forest health and productivity. The breadth and consistency of the relationships we found is testament to the strong predictive capacity of

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LiDAR-based measures of forest structure for elucidating associated patterns of tree function and incipient decline. Indeed, as forests face more biotic (e.g., in-vasive pests and pathogens) and abiotic (e.g., climate, pollutant and nutrient) stressors, an improved ability to quantify forest health and productivity remotely via technologies like LiDAR will likely have greater demand and utility.

Acknowledgements

We are grateful to all of those who provided technical assistance in the lab and in the field. They include, Sean MacFaden, Josh Halman, Paula Murakami, Ben Engel, Dan Hale, Ali Kosiba, Kindle Loomis, Allison Middleman, Allyson Ma-kuch, Jarlath O’Neil-Dunne, Nick Rodenhouse, and Helen Yurchenco. This re-search relied heavily on the assistance of staff at HBEF, especially Ian Halm. This manuscript is a contribution of the Hubbard Brook Ecosystem Study. Hubbard Brook is part of the Long-Term Ecological Research (LTER) network, which is supported by the National Science Foundation. The Hubbard Brook Experi-mental Forest is operated and maintained by the USDA Forest Service, Northern Research Station, Newtown Square, PA. This research was supported by funds provided by the Northeastern States Research Cooperative and the USDA CSREES McIntire-Stennis Forest Research Program.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this pa-per.

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